食品工业科技2025，Vol.46Issue(6)：20-29,10.DOI:10.13386/j.issn1002-0306.2024040375
深度学习在食品质量与安全检测中的应用进展
Advance in Application of Deep Learning in Food Quality and Safety Detection
摘要
Abstract
With the improvement of people's living standards,consumers'demand for food quality and safety is growing.Traditional methods for detecting food quality and safety can no longer meet the demand for efficient,accurate and reliable detection.Therefore,it becomes imperative to seek a more efficient and convenient detection method.On this basis,the rapid development of deep neural network-based machine learning technology,i.e.,deep learning,has brought new opportunities for food quality and safety detection.This paper focuses on the application progress of deep learning in food quality and safety detection.It introduces the principles of traditional machine learning and deep learning,and elaborates on the applications of deep learning in food origin tracing and food quality,including the detection of food defects,freshness,adulteration,and pathogens.Furthermore,it looks forward to the development trends of deep learning in the field of food quality and safety detection,aiming to provide theoretical references and research ideas for this field.关键词
深度学习/神经网络/食品质量与安全/食品溯源/品质检测Key words
deep learning/neural networks/food quality and safety/food traceability/quality detection分类
轻工业引用本文复制引用
郭兴,孙莹,刘树萍,杨雪欣,赵钜阳,江连洲..深度学习在食品质量与安全检测中的应用进展[J].食品工业科技,2025,46(6):20-29,10.基金项目
国家自然科学基金面上项目(32372386) （32372386）
黑龙江省自然科学基金项目(LH2022C048). （LH2022C048）
